Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/90358
Title: Yin-Yang firefly algorithm based on dimensionally Cauchy mutation
Authors: Wang, WC
Xu, L
Chau, KW 
Xu, DM
Issue Date: Jul-2020
Source: Expert systems with applications, July 2020, v. 150, 113216
Abstract: Firefly algorithm (FA) is a classical and efficient swarm intelligence optimization method and has a natural capability to address multimodal optimization. However, it suffers from premature convergence and low stability in the solution quality. In this paper, a Yin-Yang firefly algorithm (YYFA) based on dimensionally Cauchy mutation is proposed for performance improvement of FA. An initial position of fireflies is specified by the good nodes set (GNS) strategy to ensure the spatial representativeness of the firefly population. A designed random attraction model is then used in the proposed work to reduce the time complexity of the algorithm. Besides, a key self-learning procedure on the brightest firefly is undertaken to strike a balance between exploration and exploitation. The performance of the proposed algorithm is verified by a set of CEC 2013 benchmark functions used for the single objective real parameter algorithm competition. Experimental results are compared with those of other the state-of-the-art variants of FA. Nonparametric statistical tests on the results demonstrate that YYFA provides highly competitive performance in terms of the tested algorithms. In addition, the application in constrained engineering optimization problems shows the practicability of YYFA algorithm.
Keywords: Cauchy mutation
CEC 2013 benchmark functions
Engineering optimization problems
GNS strategy
Random attraction model
Yin-Yang firefly algorithm
Publisher: Pergamon Press
Journal: Expert systems with applications 
ISSN: 0957-4174
EISSN: 1873-6793
DOI: 10.1016/j.eswa.2020.113216
Appears in Collections:Journal/Magazine Article

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